Results

Imports

library(stargazer)
library(tidyverse)
library(patchwork)
library(plotly)

load("data/models.RData")

wahl_lohn <- readRDS("data/wahl_lohn_mod.rds")

Plots

Basic plot with SB

plot_basic <- ggplot(aes(x = lohn_prozent, y = afd_prozent, label=name), data = wahl_lohn) +
  geom_point(shape = 1) +
  geom_smooth(method = "lm", se = T) +
  labs(
    x = "Minimum Wage Share",
    y = "AfD Vote Share",
    subtitle = "District Level",
    caption = "Data: WSI, Bundeswahlleiter"
  ) +
  theme_gray() #+ #scientific theme
  #coord_cartesian(xlim = c(0, 0.5), ylim = c(0, 0.35)) + #start at 0,0
  # for Sonneberg (16072)
  # geom_point(
  #   data = wahl_lohn %>% filter("kreis" == "16072"),
  #   color = "red",
  # ) + 
  # geom_text(
  #   data = wahl_lohn %>% filter("kreis" == "16072"),
  #   label = "SB",
  #   vjust = 1.3,
  #   hjust = 0,
  #   color = "red"
  # )
ggsave("images/plot_basic.png", plot_basic, width = 10, height = 7)

`geom_smooth()` using formula 'y ~ x'

#plotly::ggplotly(plot_basic)
plot_basic

`geom_smooth()` using formula 'y ~ x'

Plot with east germany

ggplot(aes(x = lohn_prozent, y = afd_prozent), data = wahl_lohn) +
  geom_point(aes(color = factor(ost))) +
  geom_smooth(method = "lm") +
  labs(
    x = "Anteil Mindestlohnbezieher",
    y = "AfD Stimmen in %",
    title = "AfD Stimmen und Mindestlohnbezieher",
    subtitle = "Kreisebene",
    caption = "Daten: WSI, Bundeswahlleiter"
  ) +
  scale_color_manual(values = c("red", "blue"), name="Osten") + #color of points
  theme_minimal() + #scientific theme
  coord_cartesian(xlim = c(0, 0.5), ylim = c(0, 0.35))  #start at 0,0

`geom_smooth()` using formula 'y ~ x'

Plot with scale for arbeitslosenquote

ggplot(aes(x = lohn_prozent, y = afd_prozent), data = wahl_lohn) +
  geom_point(aes(color = arbeitslosenquote)) +
  geom_smooth(method = "lm") +
  labs(
    x = "Anteil Mindestlohnbezieher",
    y = "AfD Stimmen in %",
    title = "AfD Stimmen und Mindestlohnbezieher",
    subtitle = "Kreisebene",
    caption = "Daten: WSI, Bundeswahlleiter"
  ) +
  scale_color_viridis_c(name = "Arbeitslosenquote", option="magma", direction=-1) + #color of points
  theme_minimal() + #scientific theme
  coord_cartesian(xlim = c(0, 0.5), ylim = c(0, 0.35))  #start at 0,0

`geom_smooth()` using formula 'y ~ x'

Plot for linkspartei

ggplot(aes(x = lohn_prozent, y = linke_prozent), data = wahl_lohn) +
  geom_point() +
  geom_smooth(method = "lm") +
  theme_minimal() #scientific theme

`geom_smooth()` using formula 'y ~ x'

Warning: Removed 1 rows containing non-finite values (stat_smooth).

Warning: Removed 1 rows containing missing values (geom_point).

Maps

Load GEOJSON Data for Kreise

Data Source = Regionalatlas Statistikportal

spdf <- sf::read_sf("data/kreise.geojson")

Merge

geodata <- spdf %>% 
  left_join(wahl_lohn, by = c("schluessel" = "kreis"))

Afd Stimmen

afd_map <- ggplot(geodata) +
  geom_sf(aes(fill = afd_prozent)) +
  scale_fill_viridis_c(name = "AfD in %", option="magma", direction=-1) +
  theme_minimal() +
  labs(
    title = "AfD Vote Share",
    subtitle = "District Level",
    caption = "Data: Bundeswahlleiter"
  )

Mindestlohn

lohn_map <- ggplot(geodata) +
  geom_sf(aes(fill = lohn_prozent)) +
  scale_fill_viridis_c(name = "MW %", option="magma", direction=-1) +
  theme_minimal() +
  labs(
    title = "Minimum Wage Recipients",
    subtitle = "District Level",
    caption = "Data: WSI"
  )

both maps next to each other

maps <- afd_map + lohn_map
ggsave("images/maps.png",plot=maps, width = 10, height = 5)

subplot(ggplotly(afd_map), ggplotly(lohn_map))

Regression Models Table

Summary Statistic of Data

stargazer(as.data.frame(wahl_lohn %>% subset(select = -foreigners)), type="html")

Statistic	N	Mean	St. Dev.	Min	Max
afd_prozent	400	0.113	0.058	0.029	0.321
lohn_prozent	400	0.194	0.064	0.079	0.440
linke_prozent	399	0.045	0.029	0.015	0.155
ost	400	0.190	0.393	0	1
arbeitslosenquote	400	0.052	0.022	0.019	0.148
gdp	400	40,329.500	16,743.150	17,553	158,749
age	400	45.277	2.008	40.700	51.000
pop	400	536.540	708.669	35.300	4,788.200

output to latex / html for inclusion in report

stargazer(as.data.frame(wahl_lohn %>% subset(select = -foreigners)), type="latex", out="images/summary_table.tex", header = FALSE)
stargazer(as.data.frame(wahl_lohn %>% subset(select = -foreigners)), type="html", out="images/summary_table.html", header = FALSE)

Compile List of Models to include in Table

models <- list(
  model_basic,
  model_ost,
  model_arbeit,
  model_gdp,
  model_age,
  model_pop
)

Create the Stargazer Table

Table 1

table <- stargazer(models, 
          type = "html", #change to text for coding
          title = "Effect of Minimum Wage Recipients on AfD Vote Share",
          dep.var.labels = "AfD Vote Share",
          covariate.labels = c(
            "Minimum Wage Rate", 
            "East Germany",
            "Unemployment Rate", 
            "Log GDP p. C.", 
            "Avg. Age", 
            "Log Pop. Density"
            ),
          df = F,
          omit.stat = c("rsq", "f")
          )

Effect of Minimum Wage Recipients on AfD Vote Share

	Dependent variable:
	AfD Vote Share
	(1)	(2)	(3)	(4)	(5)	(6)
Minimum Wage Rate	0.714***	0.291***	0.308***	0.301***	0.227***	0.220***
	(0.028)	(0.039)	(0.039)	(0.044)	(0.044)	(0.044)
East Germany		0.086***	0.088***	0.088***	0.081***	0.082***
		(0.006)	(0.006)	(0.006)	(0.006)	(0.006)
Unemployment Rate			-0.279***	-0.275***	-0.244***	-0.163*
			(0.071)	(0.072)	(0.070)	(0.093)
Log GDP p. C.				-0.002	0.011*	0.013**
				(0.005)	(0.006)	(0.006)
Avg. Age					0.006***	0.006***
					(0.001)	(0.001)
Log Pop. Density						-0.003
						(0.002)
Constant	-0.026***	0.040***	0.051***	0.071	-0.332***	-0.315***
	(0.006)	(0.007)	(0.007)	(0.060)	(0.091)	(0.092)
Observations	400	400	400	400	400	400
Adjusted R2	0.612	0.732	0.742	0.741	0.760	0.761
Residual Std. Error	0.036	0.030	0.030	0.030	0.029	0.029
Note:	p<0.1; p<0.05; p<0.01

output it to latex / html for inclusion in Report

stargazer(models, 
          type = "latex",
          out= "images/table.tex",
          header = FALSE,
          title = "Effect of Minimum Wage Recipients on AfD Vote Share",
          dep.var.labels = "AfD Vote Share",
          covariate.labels = c(
            "Minimum Wage Rate", 
            "East Germany",
            "Unemployment Rate", 
            "Log GDP p. C.", 
            "Avg. Age", 
            "Log Pop. Density"
            ),
          df = F,
          omit.stat = c("rsq", "f")
          )

stargazer(models, 
          type = "html",
          out= "images/table.html",
          header = FALSE,
          title = "Effect of Minimum Wage Recipients on AfD Vote Share",
          dep.var.labels = "AfD Vote Share",
          covariate.labels = c(
            "Minimum Wage Rate", 
            "East Germany",
            "Unemployment Rate", 
            "Log GDP p. C.", 
            "Avg. Age", 
            "Log Pop. Density"
            ),
          df = F,
          omit.stat = c("rsq", "f")
          )

Rewrite this code with table package gt